# ALBU: An approximate Loopy Belief message passing algorithm for LDA to improve performance on small data sets

arXiv: https://arxiv.org/abs/2110.00635 · HTML: https://mlsanity.com/paper/2110.00635/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 49 references that are in the map.
- First posted on arXiv 1 Oct 2021.

## Builds on (most cited first, 4 of 4)

- [Variational Inference: A Review for Statisticians](https://mlsanity.com/paper/1601.00670/) (2016)
- [Stochastic Collapsed Variational Bayesian Inference for Latent Dirichlet Allocation](https://mlsanity.com/paper/1305.2452/) (2013)
- [Investor Reaction to Financial Disclosures Across Topics: An Application of Latent Dirichlet Allocation](https://mlsanity.com/paper/1805.03308/) (2018)
- [Topic Modelling and Event Identification from Twitter Textual Data](https://mlsanity.com/paper/1608.02519/) (2016)

## Built on by (most cited first, 1 of 1)

- [A derivation of variational message passing (VMP) for latent Dirichlet allocation (LDA)](https://mlsanity.com/paper/2111.01480/) (2021)

## Abstract (arXiv)

Variational Bayes (VB) applied to latent Dirichlet allocation (LDA) has become the most popular algorithm for aspect modeling. While sufficiently successful in text topic extraction from large corpora, VB is less successful in identifying aspects in the presence of limited data. We present a novel variational message passing algorithm as applied to Latent Dirichlet Allocation (LDA) and compare it with the gold standard VB and collapsed Gibbs sampling. In situations where marginalisation leads to non-conjugate messages, we use ideas from sampling to derive approximate update equations. In cases where conjugacy holds, Loopy Belief update (LBU) (also known as Lauritzen-Spiegelhalter) is used. Our algorithm, ALBU (approximate LBU), has strong similarities with Variational Message Passing (VMP) (which is the message passing variant of VB). To compare the performance of the algorithms in the presence of limited data, we use data sets consisting of tweets and news groups. Additionally, to perform more fine grained evaluations and comparisons, we use simulations that enable comparisons with the ground truth via Kullback-Leibler divergence (KLD). Using coherence measures for the text corpora and KLD with the simulations we show that ALBU learns latent distributions more accurately than does VB, especially for smaller data sets.